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Most vehicle classification systems now use data from images or videos. However, these approaches violate drivers' privacy and reveal their identities. Due to various disruptions, detecting automobiles using seismic ambient noise signals is challenging. This study uses seismic surface waves to compare time series data between different vehicle types. We applied various artificial intelligence approaches using raw data from three different vehicle sizes (Bus/Truck, Car, and Motorcycle) and background noise. By using vertical component seismic data, this study compares the decoding abilities of Logistic Regression, Support Vector Machine, and Naïve Bayes (NB) approaches to determine the class of automobiles. The Multiclass classifiers were trained on 4185 samples and tested on 1395 randomly chosen from actual and synthetic data sets. Additionally, we used the convolutional neural network approach as a baseline to assess the effectiveness of machine learning (ML) methods. The NB method showed relatively high classification accuracy during training for the three multiclass classification situations. Overall, we investigate an ML-based decoding technique that can be used for security and traffic analysis across large geographic areas without endangering driver privacy and is more effective and economical than conventional methods. [ABSTRACT FROM AUTHOR]

In this study, a new paradigm containing motor observation, motor execution, and motor imagery was designed to investigate whether motor imagery (MI) and motor execution (ME) of finger gestures can be used to extend commands of practical mBCIs. The subjects were instructed to perform or imagine 30 left and right finger gestures. Hierarchical support vector machine (hSVM) method was applied to classify four tasks (i.e., ME and MI tasks between left and right gestures). The average classification accuracies of motor imagery and execution tasks using fivefold cross-validation were 90.89 ± 9.87% and 74.08 ± 13.42% in first layer and second layer, respectively. The average accuracy of classification of four classes is 83.06 ± 7.29% overall. These results show that performing or imaging finger movements have the potential to extend the commands of the existing BCI, especially for healthy elderly living. [ABSTRACT FROM AUTHOR]

A hybrid brain-computer interface (hBCI) has recently been proposed to address the limitations of existing single-modal brain computer interfaces (BCIs) in terms of accuracy and information transfer rate (ITR) by combining more than one modality. The hBCI system also showed promising prospects for patients because the design of a human-centered smart robot control system may allow the performance of multiple tasks with high efficiency. In this paper, we present a hybrid multicontrol system that simultaneously uses electroencephalography (EEG) and electrooculography (EOG) signals. After the preprocessing phase, we used a common spatial pattern (CSP) algorithm to extract EEG and EOG features from motor imagery and eye movements. Moreover, a support vector machine (SVM) was used to solve a multiclass problem and complete flight operations through the asynchronous hBCI control of a four-axis quadcopter (e.g., takeoff, forward, backward, rightward, leftward, and landing). Online decoding of experimental results showed that 97.14, 95.23, 98.09, and 96.66% average accuracies, and 45.80, 43.99, 46.78, and 45.34 bits/min average ITRs were achieved in the control of a quadcopter. These online experimental results showed that the proposed hybrid system might be better in terms of completing multidirection control tasks to increase the multitasking and dimensionality of a BCI. [ABSTRACT FROM AUTHOR]

Pattern classification algorithm is the crucial step in developing brain-computer interface (BCI) applications. In this paper, a hierarchical support vector machine (HSVM) algorithm is proposed to address an EEG-based four-class motor imagery classification task. Wavelet packet transform is employed to decompose raw EEG signals. Thereafter, EEG signals with effective frequency sub-bands are grouped and reconstructed. EEG feature vectors are extracted from the reconstructed EEG signals with one versus the rest common spatial patterns (OVR-CSP) and one versus one common spatial patterns (OVO-CSP). Then, a two-layer HSVM algorithm is designed for the classification of these EEG feature vectors, where "OVO" classifiers are used in the first layer and "OVR" in the second layer. A public dataset (BCI Competition IV-II-a)is employed to validate the proposed method. Fivefold cross-validation results demonstrate that the average accuracy of classification in the first layer and the second layer is 67.5 ± 17.7% and 60.3 ± 14.7%, respectively. The average accuracy of the classification is 64.4 ± 16.7% overall. These results show that the proposed method is effective for four-class motor imagery classification. [ABSTRACT FROM AUTHOR]

To provide multidimensional control, we describe the first reported decoding of bilateral hand movements by using single-trial magnetoencephalography signals as a new approach to enhance a user’s ability to interact with a complex environment through a multidimensional brain–machine interface. Ten healthy participants performed or imagined four types of bilateral hand movements during neuromagnetic measurements. By applying a support vector machine (SVM) method to classify the four movements regarding the sensor data obtained from the sensorimotor area, we found the mean accuracy of a two-class classification using the amplitudes of neuromagnetic fields to be particularly suitable for real-time applications, with accuracies comparable to those obtained in previous studies involving unilateral movement. The sensor data from over the sensorimotor cortex showed discriminative time-series waveforms and time-frequency maps in the bilateral hemispheres according to the four tasks. Furthermore, we used four-class classification algorithms based on the SVM method to decode all types of bilateral movements. Our results provided further proof that the slow components of neuromagnetic fields carry sufficient neural information to classify even bilateral hand movements and demonstrated the potential utility of decoding bilateral movements for engineering purposes such as multidimensional motor control. [ABSTRACT FROM AUTHOR]